Overview

Dataset statistics

Number of variables31
Number of observations502749
Missing cells2607548
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.9 MiB
Average record size in memory248.0 B

Variable types

Numeric20
DateTime1
Categorical4
Text6

Alerts

CANCELLED is highly imbalanced (87.0%)Imbalance
DIVERTED is highly imbalanced (98.0%)Imbalance
DEP_TIME has 8815 (1.8%) missing valuesMissing
DEP_DELAY has 8815 (1.8%) missing valuesMissing
TAXI_OUT has 8938 (1.8%) missing valuesMissing
TAXI_IN has 9173 (1.8%) missing valuesMissing
ARR_TIME has 9173 (1.8%) missing valuesMissing
ARR_DELAY has 10002 (2.0%) missing valuesMissing
CANCELLATION_CODE has 493730 (98.2%) missing valuesMissing
AIR_TIME has 10002 (2.0%) missing valuesMissing
CARRIER_DELAY has 409780 (81.5%) missing valuesMissing
WEATHER_DELAY has 409780 (81.5%) missing valuesMissing
NAS_DELAY has 409780 (81.5%) missing valuesMissing
SECURITY_DELAY has 409780 (81.5%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 409780 (81.5%) missing valuesMissing
WEATHER_DELAY is highly skewed (γ1 = 21.7330739)Skewed
SECURITY_DELAY is highly skewed (γ1 = 154.3624039)Skewed
DEP_DELAY has 22838 (4.5%) zerosZeros
ARR_DELAY has 9023 (1.8%) zerosZeros
CARRIER_DELAY has 40359 (8.0%) zerosZeros
WEATHER_DELAY has 88146 (17.5%) zerosZeros
NAS_DELAY has 47985 (9.5%) zerosZeros
SECURITY_DELAY has 92531 (18.4%) zerosZeros
LATE_AIRCRAFT_DELAY has 49855 (9.9%) zerosZeros

Reproduction

Analysis started2024-03-30 05:56:58.637813
Analysis finished2024-03-30 05:59:44.171246
Duration2 minutes and 45.53 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9670153
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:44.280931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9989356
Coefficient of variation (CV)0.50388904
Kurtosis-1.2281979
Mean3.9670153
Median Absolute Deviation (MAD)2
Skewness0.019561677
Sum1994413
Variance3.9957434
MonotonicityIncreasing
2024-03-30T02:59:44.535186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 75323
15.0%
4 74952
14.9%
1 74941
14.9%
7 72282
14.4%
3 70645
14.1%
2 70624
14.0%
6 63982
12.7%
ValueCountFrequency (%)
1 74941
14.9%
2 70624
14.0%
3 70645
14.1%
4 74952
14.9%
5 75323
15.0%
6 63982
12.7%
7 72282
14.4%
ValueCountFrequency (%)
7 72282
14.4%
6 63982
12.7%
5 75323
15.0%
4 74952
14.9%
3 70645
14.1%
2 70624
14.0%
1 74941
14.9%
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
Minimum2023-02-01 00:00:00
Maximum2023-02-28 00:00:00
2024-03-30T02:59:44.811662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:45.091588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
WN
101455 
AA
71289 
DL
69152 
UA
53408 
OO
50486 
Other values (10)
156959 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1005498
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 101455
20.2%
AA 71289
14.2%
DL 69152
13.8%
UA 53408
10.6%
OO 50486
10.0%
YX 23728
 
4.7%
B6 22186
 
4.4%
NK 20192
 
4.0%
AS 17913
 
3.6%
MQ 17228
 
3.4%
Other values (5) 55712
11.1%

Length

2024-03-30T02:59:45.366082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 101455
20.2%
aa 71289
14.2%
dl 69152
13.8%
ua 53408
10.6%
oo 50486
10.0%
yx 23728
 
4.7%
b6 22186
 
4.4%
nk 20192
 
4.0%
as 17913
 
3.6%
mq 17228
 
3.4%
Other values (5) 55712
11.1%

Most occurring characters

ValueCountFrequency (%)
A 219896
21.9%
N 121647
12.1%
O 115022
11.4%
W 101455
10.1%
D 69152
 
6.9%
L 69152
 
6.9%
U 53408
 
5.3%
9 27157
 
2.7%
Y 23728
 
2.4%
X 23728
 
2.4%
Other values (11) 181153
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1005498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 219896
21.9%
N 121647
12.1%
O 115022
11.4%
W 101455
10.1%
D 69152
 
6.9%
L 69152
 
6.9%
U 53408
 
5.3%
9 27157
 
2.7%
Y 23728
 
2.4%
X 23728
 
2.4%
Other values (11) 181153
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1005498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 219896
21.9%
N 121647
12.1%
O 115022
11.4%
W 101455
10.1%
D 69152
 
6.9%
L 69152
 
6.9%
U 53408
 
5.3%
9 27157
 
2.7%
Y 23728
 
2.4%
X 23728
 
2.4%
Other values (11) 181153
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1005498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 219896
21.9%
N 121647
12.1%
O 115022
11.4%
W 101455
10.1%
D 69152
 
6.9%
L 69152
 
6.9%
U 53408
 
5.3%
9 27157
 
2.7%
Y 23728
 
2.4%
X 23728
 
2.4%
Other values (11) 181153
18.0%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5819
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2213.2226
Minimum1
Maximum8816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:45.641285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile272
Q1983
median1922
Q33062
95-th percentile5355
Maximum8816
Range8815
Interquartile range (IQR)2079

Descriptive statistics

Standard deviation1559.1208
Coefficient of variation (CV)0.7044573
Kurtosis-0.40677493
Mean2213.2226
Median Absolute Deviation (MAD)1020
Skewness0.72606599
Sum1.1126954 × 109
Variance2430857.7
MonotonicityNot monotonic
2024-03-30T02:59:46.165553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
710 300
 
0.1%
1370 281
 
0.1%
568 280
 
0.1%
531 277
 
0.1%
366 277
 
0.1%
1168 276
 
0.1%
2005 275
 
0.1%
2274 273
 
0.1%
1318 267
 
0.1%
334 266
 
0.1%
Other values (5809) 499977
99.4%
ValueCountFrequency (%)
1 120
< 0.1%
2 159
< 0.1%
3 182
< 0.1%
4 194
< 0.1%
5 68
 
< 0.1%
6 92
< 0.1%
7 103
< 0.1%
8 101
< 0.1%
9 80
< 0.1%
10 161
< 0.1%
ValueCountFrequency (%)
8816 1
 
< 0.1%
8815 1
 
< 0.1%
8814 4
< 0.1%
8813 3
< 0.1%
8812 1
 
< 0.1%
8809 1
 
< 0.1%
8808 2
< 0.1%
8807 2
< 0.1%
8806 1
 
< 0.1%
8803 1
 
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12651.492
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:46.504793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1522.1633
Coefficient of variation (CV)0.12031492
Kurtosis-1.2890411
Mean12651.492
Median Absolute Deviation (MAD)1568
Skewness0.10429836
Sum6.3605249 × 109
Variance2316981.1
MonotonicityNot monotonic
2024-03-30T02:59:46.842773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 24126
 
4.8%
11292 20653
 
4.1%
11298 19910
 
4.0%
13930 19329
 
3.8%
11057 14234
 
2.8%
12889 14116
 
2.8%
12892 13973
 
2.8%
14107 13700
 
2.7%
12953 13203
 
2.6%
13204 12400
 
2.5%
Other values (329) 337105
67.1%
ValueCountFrequency (%)
10135 288
 
0.1%
10136 80
 
< 0.1%
10140 1559
0.3%
10141 55
 
< 0.1%
10146 68
 
< 0.1%
10155 81
 
< 0.1%
10157 136
 
< 0.1%
10158 217
 
< 0.1%
10165 8
 
< 0.1%
10170 48
 
< 0.1%
ValueCountFrequency (%)
16869 123
 
< 0.1%
16218 136
 
< 0.1%
15991 56
 
< 0.1%
15919 777
0.2%
15841 56
 
< 0.1%
15624 496
0.1%
15607 68
 
< 0.1%
15582 48
 
< 0.1%
15569 48
 
< 0.1%
15412 897
0.2%

ORIGIN
Text

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:47.521737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1508247
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowDTW
3rd rowGRB
4th rowATL
5th rowFAY
ValueCountFrequency (%)
atl 24126
 
4.8%
den 20653
 
4.1%
dfw 19910
 
4.0%
ord 19329
 
3.8%
clt 14234
 
2.8%
las 14116
 
2.8%
lax 13973
 
2.8%
phx 13700
 
2.7%
lga 13203
 
2.6%
mco 12400
 
2.5%
Other values (329) 337105
67.1%
2024-03-30T02:59:48.528297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 172302
 
11.4%
L 139636
 
9.3%
S 128183
 
8.5%
D 117109
 
7.8%
T 78596
 
5.2%
O 77395
 
5.1%
C 75973
 
5.0%
M 66645
 
4.4%
F 62080
 
4.1%
W 58604
 
3.9%
Other values (16) 531724
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 172302
 
11.4%
L 139636
 
9.3%
S 128183
 
8.5%
D 117109
 
7.8%
T 78596
 
5.2%
O 77395
 
5.1%
C 75973
 
5.0%
M 66645
 
4.4%
F 62080
 
4.1%
W 58604
 
3.9%
Other values (16) 531724
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 172302
 
11.4%
L 139636
 
9.3%
S 128183
 
8.5%
D 117109
 
7.8%
T 78596
 
5.2%
O 77395
 
5.1%
C 75973
 
5.0%
M 66645
 
4.4%
F 62080
 
4.1%
W 58604
 
3.9%
Other values (16) 531724
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 172302
 
11.4%
L 139636
 
9.3%
S 128183
 
8.5%
D 117109
 
7.8%
T 78596
 
5.2%
O 77395
 
5.1%
C 75973
 
5.0%
M 66645
 
4.4%
F 62080
 
4.1%
W 58604
 
3.9%
Other values (16) 531724
35.3%
Distinct333
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:49.061864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.027043
Min length8

Characters and Unicode

Total characters6549333
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowDetroit, MI
3rd rowGreen Bay, WI
4th rowAtlanta, GA
5th rowFayetteville, NC
ValueCountFrequency (%)
ca 53600
 
4.6%
tx 51571
 
4.4%
fl 47508
 
4.0%
ny 29961
 
2.6%
new 27756
 
2.4%
il 26141
 
2.2%
ga 25880
 
2.2%
san 25419
 
2.2%
chicago 25179
 
2.1%
atlanta 24126
 
2.1%
Other values (404) 837434
71.3%
2024-03-30T02:59:49.877428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
671826
 
10.3%
, 502749
 
7.7%
a 498054
 
7.6%
o 361670
 
5.5%
e 346506
 
5.3%
n 319899
 
4.9%
t 309295
 
4.7%
l 282248
 
4.3%
i 245001
 
3.7%
r 239475
 
3.7%
Other values (46) 2772610
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6549333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
671826
 
10.3%
, 502749
 
7.7%
a 498054
 
7.6%
o 361670
 
5.5%
e 346506
 
5.3%
n 319899
 
4.9%
t 309295
 
4.7%
l 282248
 
4.3%
i 245001
 
3.7%
r 239475
 
3.7%
Other values (46) 2772610
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6549333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
671826
 
10.3%
, 502749
 
7.7%
a 498054
 
7.6%
o 361670
 
5.5%
e 346506
 
5.3%
n 319899
 
4.9%
t 309295
 
4.7%
l 282248
 
4.3%
i 245001
 
3.7%
r 239475
 
3.7%
Other values (46) 2772610
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6549333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
671826
 
10.3%
, 502749
 
7.7%
a 498054
 
7.6%
o 361670
 
5.5%
e 346506
 
5.3%
n 319899
 
4.9%
t 309295
 
4.7%
l 282248
 
4.3%
i 245001
 
3.7%
r 239475
 
3.7%
Other values (46) 2772610
42.3%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:50.336919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1390475
Min length4

Characters and Unicode

Total characters4091898
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowMichigan
3rd rowWisconsin
4th rowGeorgia
5th rowNorth Carolina
ValueCountFrequency (%)
california 53600
 
9.3%
texas 51571
 
8.9%
florida 47508
 
8.2%
new 43553
 
7.5%
york 29961
 
5.2%
illinois 26141
 
4.5%
georgia 25880
 
4.5%
carolina 24222
 
4.2%
colorado 24087
 
4.2%
north 21805
 
3.8%
Other values (51) 229998
39.8%
2024-03-30T02:59:51.103670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 546066
13.3%
i 459458
 
11.2%
o 394927
 
9.7%
r 300894
 
7.4%
n 295883
 
7.2%
e 252291
 
6.2%
s 230151
 
5.6%
l 227643
 
5.6%
C 103676
 
2.5%
d 103038
 
2.5%
Other values (37) 1177871
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4091898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 546066
13.3%
i 459458
 
11.2%
o 394927
 
9.7%
r 300894
 
7.4%
n 295883
 
7.2%
e 252291
 
6.2%
s 230151
 
5.6%
l 227643
 
5.6%
C 103676
 
2.5%
d 103038
 
2.5%
Other values (37) 1177871
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4091898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 546066
13.3%
i 459458
 
11.2%
o 394927
 
9.7%
r 300894
 
7.4%
n 295883
 
7.2%
e 252291
 
6.2%
s 230151
 
5.6%
l 227643
 
5.6%
C 103676
 
2.5%
d 103038
 
2.5%
Other values (37) 1177871
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4091898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 546066
13.3%
i 459458
 
11.2%
o 394927
 
9.7%
r 300894
 
7.4%
n 295883
 
7.2%
e 252291
 
6.2%
s 230151
 
5.6%
l 227643
 
5.6%
C 103676
 
2.5%
d 103038
 
2.5%
Other values (37) 1177871
28.8%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.229265
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:51.514265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)49

Descriptive statistics

Standard deviation26.848321
Coefficient of variation (CV)0.49508916
Kurtosis-1.3323253
Mean54.229265
Median Absolute Deviation (MAD)22
Skewness-0.011996634
Sum27263709
Variance720.83236
MonotonicityNot monotonic
2024-03-30T02:59:51.891488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 53600
 
10.7%
74 51571
 
10.3%
33 47508
 
9.4%
22 29961
 
6.0%
41 26141
 
5.2%
34 25880
 
5.1%
82 24087
 
4.8%
36 20495
 
4.1%
38 17703
 
3.5%
81 15839
 
3.2%
Other values (42) 189964
37.8%
ValueCountFrequency (%)
1 2437
 
0.5%
2 9894
2.0%
3 2598
 
0.5%
4 549
 
0.1%
5 92
 
< 0.1%
11 1767
 
0.4%
12 958
 
0.2%
13 10730
2.1%
14 460
 
0.1%
15 1164
 
0.2%
ValueCountFrequency (%)
93 12745
 
2.5%
92 5535
 
1.1%
91 53600
10.7%
88 889
 
0.2%
87 8580
 
1.7%
86 1781
 
0.4%
85 15612
 
3.1%
84 1859
 
0.4%
83 2144
 
0.4%
82 24087
4.8%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12651.428
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:52.293337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1522.2048
Coefficient of variation (CV)0.12031882
Kurtosis-1.2891095
Mean12651.428
Median Absolute Deviation (MAD)1574
Skewness0.10437554
Sum6.3604926 × 109
Variance2317107.6
MonotonicityNot monotonic
2024-03-30T02:59:52.699943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 24119
 
4.8%
11292 20651
 
4.1%
11298 19900
 
4.0%
13930 19321
 
3.8%
11057 14246
 
2.8%
12889 14106
 
2.8%
12892 13961
 
2.8%
14107 13697
 
2.7%
12953 13200
 
2.6%
13204 12403
 
2.5%
Other values (329) 337145
67.1%
ValueCountFrequency (%)
10135 289
 
0.1%
10136 79
 
< 0.1%
10140 1559
0.3%
10141 55
 
< 0.1%
10146 68
 
< 0.1%
10155 81
 
< 0.1%
10157 136
 
< 0.1%
10158 217
 
< 0.1%
10165 8
 
< 0.1%
10170 48
 
< 0.1%
ValueCountFrequency (%)
16869 123
 
< 0.1%
16218 136
 
< 0.1%
15991 56
 
< 0.1%
15919 777
0.2%
15841 56
 
< 0.1%
15624 496
0.1%
15607 68
 
< 0.1%
15582 48
 
< 0.1%
15569 48
 
< 0.1%
15412 898
0.2%

DEST
Text

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:53.492434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1508247
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBGM
2nd rowGRB
3rd rowDTW
4th rowFAY
5th rowATL
ValueCountFrequency (%)
atl 24119
 
4.8%
den 20651
 
4.1%
dfw 19900
 
4.0%
ord 19321
 
3.8%
clt 14246
 
2.8%
las 14106
 
2.8%
lax 13961
 
2.8%
phx 13697
 
2.7%
lga 13200
 
2.6%
mco 12403
 
2.5%
Other values (329) 337145
67.1%
2024-03-30T02:59:54.632522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 172274
 
11.4%
L 139635
 
9.3%
S 128176
 
8.5%
D 117098
 
7.8%
T 78606
 
5.2%
O 77380
 
5.1%
C 76000
 
5.0%
M 66657
 
4.4%
F 62074
 
4.1%
W 58597
 
3.9%
Other values (16) 531750
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 172274
 
11.4%
L 139635
 
9.3%
S 128176
 
8.5%
D 117098
 
7.8%
T 78606
 
5.2%
O 77380
 
5.1%
C 76000
 
5.0%
M 66657
 
4.4%
F 62074
 
4.1%
W 58597
 
3.9%
Other values (16) 531750
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 172274
 
11.4%
L 139635
 
9.3%
S 128176
 
8.5%
D 117098
 
7.8%
T 78606
 
5.2%
O 77380
 
5.1%
C 76000
 
5.0%
M 66657
 
4.4%
F 62074
 
4.1%
W 58597
 
3.9%
Other values (16) 531750
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 172274
 
11.4%
L 139635
 
9.3%
S 128176
 
8.5%
D 117098
 
7.8%
T 78606
 
5.2%
O 77380
 
5.1%
C 76000
 
5.0%
M 66657
 
4.4%
F 62074
 
4.1%
W 58597
 
3.9%
Other values (16) 531750
35.3%
Distinct333
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:55.258634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.027117
Min length8

Characters and Unicode

Total characters6549370
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBinghamton, NY
2nd rowGreen Bay, WI
3rd rowDetroit, MI
4th rowFayetteville, NC
5th rowAtlanta, GA
ValueCountFrequency (%)
ca 53578
 
4.6%
tx 51562
 
4.4%
fl 47524
 
4.0%
ny 29961
 
2.6%
new 27754
 
2.4%
il 26133
 
2.2%
ga 25875
 
2.2%
san 25411
 
2.2%
chicago 25171
 
2.1%
atlanta 24119
 
2.1%
Other values (404) 837473
71.3%
2024-03-30T02:59:56.133160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
671812
 
10.3%
, 502749
 
7.7%
a 498051
 
7.6%
o 361647
 
5.5%
e 346489
 
5.3%
n 319913
 
4.9%
t 309315
 
4.7%
l 282255
 
4.3%
i 245021
 
3.7%
r 239495
 
3.7%
Other values (46) 2772623
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6549370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
671812
 
10.3%
, 502749
 
7.7%
a 498051
 
7.6%
o 361647
 
5.5%
e 346489
 
5.3%
n 319913
 
4.9%
t 309315
 
4.7%
l 282255
 
4.3%
i 245021
 
3.7%
r 239495
 
3.7%
Other values (46) 2772623
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6549370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
671812
 
10.3%
, 502749
 
7.7%
a 498051
 
7.6%
o 361647
 
5.5%
e 346489
 
5.3%
n 319913
 
4.9%
t 309315
 
4.7%
l 282255
 
4.3%
i 245021
 
3.7%
r 239495
 
3.7%
Other values (46) 2772623
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6549370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
671812
 
10.3%
, 502749
 
7.7%
a 498051
 
7.6%
o 361647
 
5.5%
e 346489
 
5.3%
n 319913
 
4.9%
t 309315
 
4.7%
l 282255
 
4.3%
i 245021
 
3.7%
r 239495
 
3.7%
Other values (46) 2772623
42.3%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:56.675371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1391728
Min length4

Characters and Unicode

Total characters4091961
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowWisconsin
3rd rowMichigan
4th rowNorth Carolina
5th rowGeorgia
ValueCountFrequency (%)
california 53578
 
9.3%
texas 51562
 
8.9%
florida 47524
 
8.2%
new 43549
 
7.5%
york 29961
 
5.2%
illinois 26133
 
4.5%
georgia 25875
 
4.5%
carolina 24237
 
4.2%
colorado 24086
 
4.2%
north 21820
 
3.8%
Other values (51) 230015
39.8%
2024-03-30T02:59:57.348434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 546057
13.3%
i 459448
 
11.2%
o 394946
 
9.7%
r 300919
 
7.4%
n 295883
 
7.2%
e 252268
 
6.2%
s 230143
 
5.6%
l 227640
 
5.6%
C 103669
 
2.5%
d 103046
 
2.5%
Other values (37) 1177942
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4091961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 546057
13.3%
i 459448
 
11.2%
o 394946
 
9.7%
r 300919
 
7.4%
n 295883
 
7.2%
e 252268
 
6.2%
s 230143
 
5.6%
l 227640
 
5.6%
C 103669
 
2.5%
d 103046
 
2.5%
Other values (37) 1177942
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4091961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 546057
13.3%
i 459448
 
11.2%
o 394946
 
9.7%
r 300919
 
7.4%
n 295883
 
7.2%
e 252268
 
6.2%
s 230143
 
5.6%
l 227640
 
5.6%
C 103669
 
2.5%
d 103046
 
2.5%
Other values (37) 1177942
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4091961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 546057
13.3%
i 459448
 
11.2%
o 394946
 
9.7%
r 300919
 
7.4%
n 295883
 
7.2%
e 252268
 
6.2%
s 230143
 
5.6%
l 227640
 
5.6%
C 103669
 
2.5%
d 103046
 
2.5%
Other values (37) 1177942
28.8%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.225516
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:57.686654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)49

Descriptive statistics

Standard deviation26.847625
Coefficient of variation (CV)0.49511054
Kurtosis-1.3322086
Mean54.225516
Median Absolute Deviation (MAD)22
Skewness-0.011797995
Sum27261824
Variance720.79495
MonotonicityNot monotonic
2024-03-30T02:59:58.035817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 53578
 
10.7%
74 51562
 
10.3%
33 47524
 
9.5%
22 29961
 
6.0%
41 26133
 
5.2%
34 25875
 
5.1%
82 24086
 
4.8%
36 20510
 
4.1%
38 17705
 
3.5%
81 15835
 
3.1%
Other values (42) 189980
37.8%
ValueCountFrequency (%)
1 2437
 
0.5%
2 9895
2.0%
3 2601
 
0.5%
4 549
 
0.1%
5 92
 
< 0.1%
11 1768
 
0.4%
12 959
 
0.2%
13 10731
2.1%
14 459
 
0.1%
15 1164
 
0.2%
ValueCountFrequency (%)
93 12742
 
2.5%
92 5539
 
1.1%
91 53578
10.7%
88 890
 
0.2%
87 8581
 
1.7%
86 1781
 
0.4%
85 15602
 
3.1%
84 1862
 
0.4%
83 2143
 
0.4%
82 24086
4.8%

CRS_DEP_TIME
Real number (ℝ)

Distinct1200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1330.1873
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:58.364875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1910
median1323
Q31735
95-th percentile2125
Maximum2359
Range2358
Interquartile range (IQR)825

Descriptive statistics

Standard deviation491.88908
Coefficient of variation (CV)0.36978933
Kurtosis-1.0693535
Mean1330.1873
Median Absolute Deviation (MAD)413
Skewness0.07973849
Sum6.6875036 × 108
Variance241954.87
MonotonicityNot monotonic
2024-03-30T02:59:58.684427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 11770
 
2.3%
700 8386
 
1.7%
800 5304
 
1.1%
900 3795
 
0.8%
630 3414
 
0.7%
730 3272
 
0.7%
830 3135
 
0.6%
1100 3086
 
0.6%
1000 2986
 
0.6%
615 2853
 
0.6%
Other values (1190) 454748
90.5%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
5 15
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
9 5
 
< 0.1%
10 13
< 0.1%
12 3
 
< 0.1%
14 13
< 0.1%
ValueCountFrequency (%)
2359 891
0.2%
2358 29
 
< 0.1%
2357 28
 
< 0.1%
2356 30
 
< 0.1%
2355 240
 
< 0.1%
2354 52
 
< 0.1%
2353 60
 
< 0.1%
2352 29
 
< 0.1%
2351 5
 
< 0.1%
2350 224
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1387
Distinct (%)0.3%
Missing8815
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean1333.7364
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T02:59:59.011416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile559
Q1913.25
median1327
Q31744
95-th percentile2135
Maximum2400
Range2399
Interquartile range (IQR)830.75

Descriptive statistics

Standard deviation503.5304
Coefficient of variation (CV)0.37753367
Kurtosis-1.0084023
Mean1333.7364
Median Absolute Deviation (MAD)416
Skewness0.029387337
Sum6.5877777 × 108
Variance253542.87
MonotonicityNot monotonic
2024-03-30T02:59:59.334034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1470
 
0.3%
556 1262
 
0.3%
557 1232
 
0.2%
554 1180
 
0.2%
558 1117
 
0.2%
559 1015
 
0.2%
655 1013
 
0.2%
553 1012
 
0.2%
657 955
 
0.2%
658 952
 
0.2%
Other values (1377) 482726
96.0%
(Missing) 8815
 
1.8%
ValueCountFrequency (%)
1 58
< 0.1%
2 43
< 0.1%
3 50
< 0.1%
4 35
< 0.1%
5 51
< 0.1%
6 32
< 0.1%
7 47
< 0.1%
8 40
< 0.1%
9 43
< 0.1%
10 47
< 0.1%
ValueCountFrequency (%)
2400 38
 
< 0.1%
2359 67
< 0.1%
2358 79
< 0.1%
2357 64
< 0.1%
2356 91
< 0.1%
2355 83
< 0.1%
2354 99
< 0.1%
2353 97
< 0.1%
2352 90
< 0.1%
2351 90
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1095
Distinct (%)0.2%
Missing8815
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean10.140636
Minimum-45
Maximum2947
Zeros22838
Zeros (%)4.5%
Negative296848
Negative (%)59.0%
Memory size3.8 MiB
2024-03-30T02:59:59.655150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile-10
Q1-6
median-2
Q37
95-th percentile68
Maximum2947
Range2992
Interquartile range (IQR)13

Descriptive statistics

Standard deviation53.240752
Coefficient of variation (CV)5.2502379
Kurtosis308.15466
Mean10.140636
Median Absolute Deviation (MAD)5
Skewness13.416682
Sum5008805
Variance2834.5777
MonotonicityNot monotonic
2024-03-30T02:59:59.991326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 38556
 
7.7%
-4 35453
 
7.1%
-3 33699
 
6.7%
-6 30522
 
6.1%
-2 30410
 
6.0%
-1 27082
 
5.4%
-7 25733
 
5.1%
0 22838
 
4.5%
-8 21120
 
4.2%
-9 16306
 
3.2%
Other values (1085) 212215
42.2%
ValueCountFrequency (%)
-45 2
 
< 0.1%
-41 2
 
< 0.1%
-38 1
 
< 0.1%
-37 2
 
< 0.1%
-35 2
 
< 0.1%
-33 4
 
< 0.1%
-32 2
 
< 0.1%
-31 10
< 0.1%
-30 9
< 0.1%
-29 13
< 0.1%
ValueCountFrequency (%)
2947 1
< 0.1%
2827 1
< 0.1%
2705 1
< 0.1%
2414 1
< 0.1%
2304 1
< 0.1%
2235 1
< 0.1%
2226 1
< 0.1%
2220 1
< 0.1%
2065 1
< 0.1%
2046 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct161
Distinct (%)< 0.1%
Missing8938
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean17.554496
Minimum1
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:00.537087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile34
Maximum175
Range174
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.4447894
Coefficient of variation (CV)0.53802682
Kurtosis19.359897
Mean17.554496
Median Absolute Deviation (MAD)4
Skewness3.17693
Sum8668603
Variance89.204047
MonotonicityNot monotonic
2024-03-30T03:00:01.085246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 39435
 
7.8%
13 39281
 
7.8%
14 37265
 
7.4%
11 36766
 
7.3%
15 33407
 
6.6%
10 30264
 
6.0%
16 30041
 
6.0%
17 25910
 
5.2%
18 22554
 
4.5%
9 21210
 
4.2%
Other values (151) 177678
35.3%
ValueCountFrequency (%)
1 5
 
< 0.1%
2 15
 
< 0.1%
3 42
 
< 0.1%
4 187
 
< 0.1%
5 607
 
0.1%
6 2344
 
0.5%
7 6351
 
1.3%
8 12587
2.5%
9 21210
4.2%
10 30264
6.0%
ValueCountFrequency (%)
175 1
 
< 0.1%
172 1
 
< 0.1%
168 1
 
< 0.1%
167 1
 
< 0.1%
166 1
 
< 0.1%
163 1
 
< 0.1%
161 2
< 0.1%
158 1
 
< 0.1%
156 3
< 0.1%
155 1
 
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct157
Distinct (%)< 0.1%
Missing9173
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean7.9077852
Minimum1
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:01.589719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum195
Range194
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.2718734
Coefficient of variation (CV)0.79312643
Kurtosis59.271668
Mean7.9077852
Median Absolute Deviation (MAD)2
Skewness5.0843466
Sum3903093
Variance39.336396
MonotonicityNot monotonic
2024-03-30T03:00:02.342187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 75127
14.9%
5 71456
14.2%
6 59218
11.8%
7 48410
9.6%
3 44412
8.8%
8 37385
7.4%
9 29180
 
5.8%
10 22943
 
4.6%
11 17608
 
3.5%
12 13804
 
2.7%
Other values (147) 74033
14.7%
ValueCountFrequency (%)
1 649
 
0.1%
2 10557
 
2.1%
3 44412
8.8%
4 75127
14.9%
5 71456
14.2%
6 59218
11.8%
7 48410
9.6%
8 37385
7.4%
9 29180
 
5.8%
10 22943
 
4.6%
ValueCountFrequency (%)
195 1
< 0.1%
193 1
< 0.1%
185 2
< 0.1%
180 1
< 0.1%
175 1
< 0.1%
171 1
< 0.1%
170 1
< 0.1%
168 1
< 0.1%
167 2
< 0.1%
163 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1298
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.6937
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:02.692653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile728
Q11109
median1523
Q31930
95-th percentile2300
Maximum2359
Range2358
Interquartile range (IQR)821

Descriptive statistics

Standard deviation516.47957
Coefficient of variation (CV)0.34439004
Kurtosis-0.49893893
Mean1499.6937
Median Absolute Deviation (MAD)411
Skewness-0.27598216
Sum7.539695 × 108
Variance266751.14
MonotonicityNot monotonic
2024-03-30T03:00:03.026782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 2539
 
0.5%
1810 1690
 
0.3%
1915 1675
 
0.3%
1025 1584
 
0.3%
2000 1553
 
0.3%
1710 1537
 
0.3%
1000 1508
 
0.3%
1750 1497
 
0.3%
1530 1471
 
0.3%
1640 1470
 
0.3%
Other values (1288) 486225
96.7%
ValueCountFrequency (%)
1 36
 
< 0.1%
2 80
 
< 0.1%
3 96
 
< 0.1%
4 84
 
< 0.1%
5 523
0.1%
6 110
 
< 0.1%
7 98
 
< 0.1%
8 56
 
< 0.1%
9 126
 
< 0.1%
10 310
0.1%
ValueCountFrequency (%)
2359 2539
0.5%
2358 524
 
0.1%
2357 808
 
0.2%
2356 544
 
0.1%
2355 906
 
0.2%
2354 505
 
0.1%
2353 413
 
0.1%
2352 295
 
0.1%
2351 249
 
< 0.1%
2350 629
 
0.1%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.3%
Missing9173
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean1476.3177
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:03.390203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile659
Q11054
median1513
Q31923
95-th percentile2255
Maximum2400
Range2399
Interquartile range (IQR)869

Descriptive statistics

Standard deviation536.19731
Coefficient of variation (CV)0.36319914
Kurtosis-0.37034272
Mean1476.3177
Median Absolute Deviation (MAD)415
Skewness-0.36477831
Sum7.2867497 × 108
Variance287507.56
MonotonicityNot monotonic
2024-03-30T03:00:03.731907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1007 566
 
0.1%
1616 565
 
0.1%
1640 556
 
0.1%
1835 551
 
0.1%
1610 550
 
0.1%
1141 549
 
0.1%
1834 547
 
0.1%
1234 546
 
0.1%
1153 545
 
0.1%
1828 542
 
0.1%
Other values (1430) 488059
97.1%
(Missing) 9173
 
1.8%
ValueCountFrequency (%)
1 320
0.1%
2 294
0.1%
3 269
0.1%
4 266
0.1%
5 290
0.1%
6 249
< 0.1%
7 231
< 0.1%
8 248
< 0.1%
9 243
< 0.1%
10 244
< 0.1%
ValueCountFrequency (%)
2400 296
0.1%
2359 338
0.1%
2358 325
0.1%
2357 330
0.1%
2356 307
0.1%
2355 341
0.1%
2354 340
0.1%
2353 366
0.1%
2352 347
0.1%
2351 367
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1136
Distinct (%)0.2%
Missing10002
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean4.1419877
Minimum-86
Maximum2949
Zeros9023
Zeros (%)1.8%
Negative313591
Negative (%)62.4%
Memory size3.8 MiB
2024-03-30T03:00:04.106941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile-29
Q1-16
median-7
Q38
95-th percentile67
Maximum2949
Range3035
Interquartile range (IQR)24

Descriptive statistics

Standard deviation55.133528
Coefficient of variation (CV)13.310887
Kurtosis263.97957
Mean4.1419877
Median Absolute Deviation (MAD)11
Skewness12.032413
Sum2040952
Variance3039.7059
MonotonicityNot monotonic
2024-03-30T03:00:04.476701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 13595
 
2.7%
-10 13575
 
2.7%
-12 13532
 
2.7%
-13 13414
 
2.7%
-9 13335
 
2.7%
-14 13075
 
2.6%
-8 12970
 
2.6%
-15 12940
 
2.6%
-7 12377
 
2.5%
-16 12313
 
2.4%
Other values (1126) 361621
71.9%
ValueCountFrequency (%)
-86 1
 
< 0.1%
-76 1
 
< 0.1%
-75 3
< 0.1%
-74 3
< 0.1%
-73 1
 
< 0.1%
-72 3
< 0.1%
-71 1
 
< 0.1%
-70 3
< 0.1%
-69 2
< 0.1%
-68 3
< 0.1%
ValueCountFrequency (%)
2949 1
< 0.1%
2837 1
< 0.1%
2764 1
< 0.1%
2294 1
< 0.1%
2225 1
< 0.1%
2216 1
< 0.1%
2214 1
< 0.1%
2067 1
< 0.1%
2021 1
< 0.1%
2019 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0.0
493730 
1.0
 
9019

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1508247
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 493730
98.2%
1.0 9019
 
1.8%

Length

2024-03-30T03:00:04.772512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:00:05.005674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 493730
98.2%
1.0 9019
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 996479
66.1%
. 502749
33.3%
1 9019
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 996479
66.1%
. 502749
33.3%
1 9019
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 996479
66.1%
. 502749
33.3%
1 9019
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 996479
66.1%
. 502749
33.3%
1 9019
 
0.6%

CANCELLATION_CODE
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing493730
Missing (%)98.2%
Memory size3.8 MiB
B
7019 
A
1726 
C
 
274

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9019
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B 7019
 
1.4%
A 1726
 
0.3%
C 274
 
0.1%
(Missing) 493730
98.2%

Length

2024-03-30T03:00:05.249102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:00:05.466705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 7019
77.8%
a 1726
 
19.1%
c 274
 
3.0%

Most occurring characters

ValueCountFrequency (%)
B 7019
77.8%
A 1726
 
19.1%
C 274
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 7019
77.8%
A 1726
 
19.1%
C 274
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 7019
77.8%
A 1726
 
19.1%
C 274
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 7019
77.8%
A 1726
 
19.1%
C 274
 
3.0%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0.0
501766 
1.0
 
983

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1508247
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 501766
99.8%
1.0 983
 
0.2%

Length

2024-03-30T03:00:05.698496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:00:05.911818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 501766
99.8%
1.0 983
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1004515
66.6%
. 502749
33.3%
1 983
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1004515
66.6%
. 502749
33.3%
1 983
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1004515
66.6%
. 502749
33.3%
1 983
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1508247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1004515
66.6%
. 502749
33.3%
1 983
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct635
Distinct (%)0.1%
Missing10002
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean115.3561
Minimum8
Maximum677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:06.215794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile36
Q163
median98
Q3145
95-th percentile269
Maximum677
Range669
Interquartile range (IQR)82

Descriptive statistics

Standard deviation71.58003
Coefficient of variation (CV)0.62051359
Kurtosis3.0183241
Mean115.3561
Median Absolute Deviation (MAD)39
Skewness1.5036401
Sum56841374
Variance5123.7007
MonotonicityNot monotonic
2024-03-30T03:00:06.550434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 4226
 
0.8%
59 4221
 
0.8%
63 4126
 
0.8%
57 4109
 
0.8%
58 4071
 
0.8%
62 4068
 
0.8%
65 4048
 
0.8%
64 4047
 
0.8%
61 3993
 
0.8%
66 3990
 
0.8%
Other values (625) 451848
89.9%
(Missing) 10002
 
2.0%
ValueCountFrequency (%)
8 4
 
< 0.1%
9 14
 
< 0.1%
10 14
 
< 0.1%
11 10
 
< 0.1%
12 6
 
< 0.1%
13 2
 
< 0.1%
14 10
 
< 0.1%
15 27
 
< 0.1%
16 65
< 0.1%
17 136
< 0.1%
ValueCountFrequency (%)
677 1
< 0.1%
670 1
< 0.1%
669 1
< 0.1%
667 1
< 0.1%
664 1
< 0.1%
662 1
< 0.1%
658 2
< 0.1%
657 1
< 0.1%
654 2
< 0.1%
653 1
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct843
Distinct (%)0.9%
Missing409780
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean25.731911
Minimum0
Maximum2947
Zeros40359
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:06.851966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q324
95-th percentile102
Maximum2947
Range2947
Interquartile range (IQR)24

Descriptive statistics

Standard deviation78.885369
Coefficient of variation (CV)3.0656631
Kurtosis178.57557
Mean25.731911
Median Absolute Deviation (MAD)5
Skewness10.62912
Sum2392270
Variance6222.9014
MonotonicityNot monotonic
2024-03-30T03:00:07.293691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40359
 
8.0%
15 1632
 
0.3%
2 1606
 
0.3%
1 1569
 
0.3%
16 1555
 
0.3%
3 1538
 
0.3%
6 1515
 
0.3%
4 1411
 
0.3%
5 1403
 
0.3%
17 1380
 
0.3%
Other values (833) 39001
 
7.8%
(Missing) 409780
81.5%
ValueCountFrequency (%)
0 40359
8.0%
1 1569
 
0.3%
2 1606
 
0.3%
3 1538
 
0.3%
4 1411
 
0.3%
5 1403
 
0.3%
6 1515
 
0.3%
7 1355
 
0.3%
8 1362
 
0.3%
9 1263
 
0.3%
ValueCountFrequency (%)
2947 1
< 0.1%
2827 1
< 0.1%
2705 1
< 0.1%
2294 1
< 0.1%
2216 1
< 0.1%
2214 1
< 0.1%
2018 1
< 0.1%
1974 1
< 0.1%
1809 1
< 0.1%
1805 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct436
Distinct (%)0.5%
Missing409780
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean3.9531887
Minimum0
Maximum1747
Zeros88146
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:07.604007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum1747
Range1747
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.743366
Coefficient of variation (CV)9.8005355
Kurtosis603.16718
Mean3.9531887
Median Absolute Deviation (MAD)0
Skewness21.733074
Sum367524
Variance1501.0484
MonotonicityNot monotonic
2024-03-30T03:00:07.913332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88146
 
17.5%
15 111
 
< 0.1%
16 109
 
< 0.1%
7 98
 
< 0.1%
17 95
 
< 0.1%
18 95
 
< 0.1%
6 88
 
< 0.1%
8 88
 
< 0.1%
23 85
 
< 0.1%
4 85
 
< 0.1%
Other values (426) 3969
 
0.8%
(Missing) 409780
81.5%
ValueCountFrequency (%)
0 88146
17.5%
1 70
 
< 0.1%
2 74
 
< 0.1%
3 79
 
< 0.1%
4 85
 
< 0.1%
5 75
 
< 0.1%
6 88
 
< 0.1%
7 98
 
< 0.1%
8 88
 
< 0.1%
9 80
 
< 0.1%
ValueCountFrequency (%)
1747 1
 
< 0.1%
1522 1
 
< 0.1%
1519 1
 
< 0.1%
1440 1
 
< 0.1%
1439 4
< 0.1%
1424 2
< 0.1%
1392 1
 
< 0.1%
1387 1
 
< 0.1%
1306 1
 
< 0.1%
1299 1
 
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct304
Distinct (%)0.3%
Missing409780
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean11.654939
Minimum0
Maximum1407
Zeros47985
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:08.255202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile48
Maximum1407
Range1407
Interquartile range (IQR)17

Descriptive statistics

Standard deviation25.542698
Coefficient of variation (CV)2.1915772
Kurtosis382.26314
Mean11.654939
Median Absolute Deviation (MAD)0
Skewness11.987287
Sum1083548
Variance652.42944
MonotonicityNot monotonic
2024-03-30T03:00:08.575737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47985
 
9.5%
1 2261
 
0.4%
15 1848
 
0.4%
16 1732
 
0.3%
2 1620
 
0.3%
3 1595
 
0.3%
17 1513
 
0.3%
4 1445
 
0.3%
5 1431
 
0.3%
18 1377
 
0.3%
Other values (294) 30162
 
6.0%
(Missing) 409780
81.5%
ValueCountFrequency (%)
0 47985
9.5%
1 2261
 
0.4%
2 1620
 
0.3%
3 1595
 
0.3%
4 1445
 
0.3%
5 1431
 
0.3%
6 1317
 
0.3%
7 1252
 
0.2%
8 1282
 
0.3%
9 1072
 
0.2%
ValueCountFrequency (%)
1407 1
< 0.1%
1233 1
< 0.1%
1219 1
< 0.1%
1129 1
< 0.1%
1023 1
< 0.1%
875 1
< 0.1%
853 1
< 0.1%
838 1
< 0.1%
730 1
< 0.1%
716 2
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct86
Distinct (%)0.1%
Missing409780
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean0.15249169
Minimum0
Maximum1460
Zeros92531
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:08.885379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1460
Range1460
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.7494156
Coefficient of variation (CV)44.260875
Kurtosis29004.095
Mean0.15249169
Median Absolute Deviation (MAD)0
Skewness154.3624
Sum14177
Variance45.554612
MonotonicityNot monotonic
2024-03-30T03:00:09.193042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92531
 
18.4%
15 26
 
< 0.1%
12 19
 
< 0.1%
9 17
 
< 0.1%
8 16
 
< 0.1%
6 15
 
< 0.1%
18 15
 
< 0.1%
7 14
 
< 0.1%
14 14
 
< 0.1%
5 13
 
< 0.1%
Other values (76) 289
 
0.1%
(Missing) 409780
81.5%
ValueCountFrequency (%)
0 92531
18.4%
1 13
 
< 0.1%
2 7
 
< 0.1%
3 10
 
< 0.1%
4 10
 
< 0.1%
5 13
 
< 0.1%
6 15
 
< 0.1%
7 14
 
< 0.1%
8 16
 
< 0.1%
9 17
 
< 0.1%
ValueCountFrequency (%)
1460 1
< 0.1%
885 1
< 0.1%
808 1
< 0.1%
302 1
< 0.1%
224 1
< 0.1%
171 1
< 0.1%
144 1
< 0.1%
138 1
< 0.1%
125 1
< 0.1%
124 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct645
Distinct (%)0.7%
Missing409780
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean24.616507
Minimum0
Maximum2225
Zeros49855
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-03-30T03:00:09.541981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327
95-th percentile110
Maximum2225
Range2225
Interquartile range (IQR)27

Descriptive statistics

Standard deviation60.737739
Coefficient of variation (CV)2.4673582
Kurtosis147.37893
Mean24.616507
Median Absolute Deviation (MAD)0
Skewness8.8912974
Sum2288572
Variance3689.073
MonotonicityNot monotonic
2024-03-30T03:00:09.847982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49855
 
9.9%
16 1089
 
0.2%
15 1084
 
0.2%
17 1012
 
0.2%
18 928
 
0.2%
21 873
 
0.2%
19 866
 
0.2%
20 861
 
0.2%
23 785
 
0.2%
14 770
 
0.2%
Other values (635) 34846
 
6.9%
(Missing) 409780
81.5%
ValueCountFrequency (%)
0 49855
9.9%
1 579
 
0.1%
2 570
 
0.1%
3 555
 
0.1%
4 559
 
0.1%
5 563
 
0.1%
6 613
 
0.1%
7 641
 
0.1%
8 632
 
0.1%
9 651
 
0.1%
ValueCountFrequency (%)
2225 1
< 0.1%
1784 1
< 0.1%
1780 1
< 0.1%
1715 1
< 0.1%
1650 1
< 0.1%
1630 1
< 0.1%
1584 1
< 0.1%
1564 1
< 0.1%
1527 1
< 0.1%
1518 1
< 0.1%

Interactions

2024-03-30T02:59:29.354561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:27.093516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:33.403652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:39.790471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:46.250486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:52.573545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:59.225737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:05.470256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:12.341732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:19.654271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:26.390916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:32.866559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:40.073819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:46.455031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:53.085547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:59.731968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:06.487749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:12.282397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:17.707254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:23.478610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:29.602938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:27.606330image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:33.747492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:40.167226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:46.563320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:52.942260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:59.531961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:05.819419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:12.667688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:20.025465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:26.711929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:33.197127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:40.455849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:46.762612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:53.427171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:00.101547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:06.799191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:12.555902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:17.976536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:23.753251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:29.822606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:27.943145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:34.102948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:40.478009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:46.851304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:53.265405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:59.814508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:06.198342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:12.984527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:20.376132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:27.012858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:33.506780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:40.770501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:47.069706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:53.735535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:00.431802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:07.070426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:12.791053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:18.236830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:24.009958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:30.124536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:28.300015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:34.482100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:40.814630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:47.176175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:53.610761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:00.216000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:06.611188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:13.323419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:20.739215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:27.340326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:33.865669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:41.101114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:47.386077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:54.115492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:00.765292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:07.384596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:13.068556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:18.480685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:24.321717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:30.360597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:28.591713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:34.768184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:41.116341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:47.441661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:53.925164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:00.536440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:06.953519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:13.638391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:21.073723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:27.670367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:34.372322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:41.400017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:47.651528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:54.632168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:01.074155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:07.661637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:13.312859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:18.721516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:24.572048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:30.602880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:28.889340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:35.126456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:41.482601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:47.786616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:54.320756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:00.830856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:07.342362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:13.997066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:21.433551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:28.204854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:34.716973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:41.723049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:47.964030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:54.975906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:01.394871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:07.985901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:13.590263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:18.975910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:25.319144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:30.823357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:29.171359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:35.423453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:41.778729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:48.123654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:54.641057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:01.123092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:07.670271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:14.335401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:21.810346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:28.576627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:35.004501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:42.049331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:48.332705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:55.299110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:01.707333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:08.315170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:13.838467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:19.214134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:25.571301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:31.055533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:29.465730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:35.716210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:42.117845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:48.402663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:55.116810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:01.405074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:08.011222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:14.633724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:22.197050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:28.882607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:35.359092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:42.368674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:48.663726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:55.637888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:02.200093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:08.591751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:14.118686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:19.462147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:25.823408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:31.288858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:29.739278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:36.037582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:42.433102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:48.693219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:55.451534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:01.684761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:08.366976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T02:58:11.708229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:18.876018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:25.739225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:32.273123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:39.356661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:45.858368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:52.364144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:59.115013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:05.622015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:11.707129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:17.104959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:22.951099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:28.833411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:34.191819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:33.045111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:39.455528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:45.868140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:52.222987image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:57:58.897362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:05.135378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:11.999998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:19.256046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:26.036617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:32.528227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:39.673136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:46.169125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:52.620205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:58:59.384038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:05.923831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:11.993503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:17.475052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:23.224052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:59:29.094769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T02:59:34.921066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:59:37.977417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
012/6/2023 12:00:00 AM9E462912953LGANew York, NYNew York2210577BGMBinghamton, NYNew York2221052058.0-7.037.02.022112205.0-6.00.0NaN0.028.0NaNNaNNaNNaNNaN
112/6/2023 12:00:00 AM9E463011433DTWDetroit, MIMichigan4311977GRBGreen Bay, WIWisconsin451000958.0-2.09.04.010481001.0-47.00.0NaN0.050.0NaNNaNNaNNaNNaN
212/6/2023 12:00:00 AM9E463011977GRBGreen Bay, WIWisconsin4511433DTWDetroit, MIMichigan4311331130.0-3.020.08.013541349.0-5.00.0NaN0.051.0NaNNaNNaNNaNNaN
312/6/2023 12:00:00 AM9E463110397ATLAtlanta, GAGeorgia3411641FAYFayetteville, NCNorth Carolina3613551352.0-3.09.04.015141456.0-18.00.0NaN0.051.0NaNNaNNaNNaNNaN
412/6/2023 12:00:00 AM9E463111641FAYFayetteville, NCNorth Carolina3610397ATLAtlanta, GAGeorgia3416001619.019.011.05.017391732.0-7.00.0NaN0.057.0NaNNaNNaNNaNNaN
512/6/2023 12:00:00 AM9E463212478JFKNew York, NYNew York2212397ITHIthaca/Cortland, NYNew York2222002152.0-8.035.03.023122309.0-3.00.0NaN0.039.0NaNNaNNaNNaNNaN
612/6/2023 12:00:00 AM9E463311042CLECleveland, OHOhio4412953LGANew York, NYNew York2210151011.0-4.015.07.011561142.0-14.00.0NaN0.069.0NaNNaNNaNNaNNaN
712/6/2023 12:00:00 AM9E463312953LGANew York, NYNew York2211042CLECleveland, OHOhio44730729.0-1.031.08.0923917.0-6.00.0NaN0.069.0NaNNaNNaNNaNNaN
812/6/2023 12:00:00 AM9E463411193CVGCincinnati, OHKentucky5211433DTWDetroit, MIMichigan43620640.020.028.07.0741759.018.00.0NaN0.044.00.018.00.00.00.0
912/6/2023 12:00:00 AM9E463510821BWIBaltimore, MDMaryland3512478JFKNew York, NYNew York2217021658.0-4.018.050.018451850.05.00.0NaN0.044.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
50273972/26/2023 12:00:00 AMYX585515016STLSt. Louis, MOMissouri6412953LGANew York, NYNew York2218291823.0-6.011.07.021592130.0-29.00.0NaN0.0109.0NaNNaNNaNNaNNaN
50274072/26/2023 12:00:00 AMYX585612953LGANew York, NYNew York2213931ORFNorfolk, VAVirginia3811001054.0-6.035.06.012381229.0-9.00.0NaN0.054.0NaNNaNNaNNaNNaN
50274172/26/2023 12:00:00 AMYX585712953LGANew York, NYNew York2211042CLECleveland, OHOhio4417001658.0-2.020.017.018571849.0-8.00.0NaN0.074.0NaNNaNNaNNaNNaN
50274272/26/2023 12:00:00 AMYX585810721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3812151210.0-5.021.05.014081408.00.00.0NaN0.092.0NaNNaNNaNNaNNaN
50274372/26/2023 12:00:00 AMYX585914730SDFLouisville, KYKentucky5211433DTWDetroit, MIMichigan43730747.017.010.021.0905916.011.00.0NaN0.058.0NaNNaNNaNNaNNaN
50274472/26/2023 12:00:00 AMYX586012339INDIndianapolis, INIndiana4212953LGANew York, NYNew York221000954.0-6.014.011.012091146.0-23.00.0NaN0.087.0NaNNaNNaNNaNNaN
50274572/26/2023 12:00:00 AMYX586110721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3810101008.0-2.034.06.012061205.0-1.00.0NaN0.077.0NaNNaNNaNNaNNaN
50274672/26/2023 12:00:00 AMYX586210785BTVBurlington, VTVermont1612953LGANew York, NYNew York2211051115.010.041.010.012331251.018.00.0NaN0.045.00.010.08.00.00.0
50274772/26/2023 12:00:00 AMYX586312953LGANew York, NYNew York2215016STLSt. Louis, MOMissouri64850844.0-6.036.06.011101055.0-15.00.0NaN0.0149.0NaNNaNNaNNaNNaN
50274872/26/2023 12:00:00 AMYX586315016STLSt. Louis, MOMissouri6412953LGANew York, NYNew York2212031201.0-2.013.016.015291514.0-15.00.0NaN0.0104.0NaNNaNNaNNaNNaN